Post on 20-Feb-2016
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An Automated Segmentation Method for Microarray Image Analysis
Wei-Bang Chen1, Chengcui Zhang1 and Wen-Lin Liu2
1Department of Computer and Information Sciences2Dept. of Management, Marketing, and Industrial Distribution
University of Alabama at Birmingham March 17, 2006
What is microarray?
DNA microarray was introduced in 1999 by Patrick Brown and Vishwanath Iyer.[1]
Microarray allows biologists to monitor gene expression level in parallel.
[1] V. R. Iyer, et al. "The transcriptional program in the response of human fibroblasts to serum," Science, v283, pp. 83-7, 1999.
Problems and motivations
Uneven backgrounda result of improper counterstain
Inner holes (a donut, comet, or overlap)manufacturing quality of the slide.
ScratchTouching the spots area accidentally
NoisesInadequate washing
A typical microarray slide
y
x
Channel 532 / Channel 635
Block 4 (30 × 30 spots)
1 4
4845
Top margin
Bottom margin
Left margin Right margin
Three-step approach
Background identification and noise removal Background identification Noise removal
Fully automatic griddingSpot segmentation
Three-step approach
Background identification and noise removal Background identification Noise removal
Fully automatic griddingSpot segmentation
Step 1.1 Background identification
Coordinate of slide
Small area 1
Small area 2
Small area 3
Small area 4
Small area 5
TG
TL
Unevenbackground
0 Slide width or height
Background intensity
Local threshold TL
Global threshold TG
Background eliminated by local threshold
Background eliminated by global threshold
Step 1.1 Background identification
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ijnm a
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To deal with the uneven background problem, we firstly divide the entire slide into small areas.
Step 1.1 Background identification
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For global threshold, we use the matrix of mean values of all pixel intensities in the small area to represent the slide.
Step 1.1 Background identification
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For local threshold, we find the minimum intensity values of each row and columns
Step 1.2 Noise removal
(A) Target pixel is signal
Signal
P1
P1'
P2
P2'
P3
P3'
P4 P5
P4'P5'
P6 P7 P8 P9 P10
P6'P7'P8'P9'P10'
P11 P12 P12' P11' Noise
P1
P1'
P2
P2'
P3
P3'
P4 P5
P4'P5'
P6 P7 P8 P9 P10
P6'P7'P8'P9'P10'
P11 P12 P12' P11'
(B) Target pixel is noise
Three-step approach
Background identification and noise removal
Fully automatic gridding Finding margins Detecting blocks Gridding
Spot segmentation
Step 2.1 Finding margins
Step 2.2 Detecting blocks
Gap between rows
Gap between blocks
Block 1
Block 2
Disrupted background pixels while crossing signals
Continuous background pixels in gaps
Few signals in this area
More signals in this area
Step 2.3 Gridding
(C) Bounding-box witheligible width forcolumn detecting
(D) Bounding-box withneither eligible height nor width
(A) Bounding-box withboth eligible height and width
(B) Bounding-box witheligible height forrow detecting
Step 2.3 Gridding
Three-step approach
Background identification and noise removal
Fully automatic griddingSpot segmentation
Step 3 Spot segmentation
2))(( wbthth mmNNN where,
N is the total number of pixels which pre-labeled as signals
Nth is the number of pixels in the white class (>th)
mb is the mean of the ‘black’ class
mw is the mean of the ‘white’ class
To minimize the intra class, we want to find a threshold th to maximize the follow formula
Step 3 Spot segmentation
Nth = NAll pixels pre-labeled as ‘foreground’ are real signals.
Nth < NPart of the ‘foreground’ pixels belong to noise, inner holes, or outer rims. (Nth / N) ≤ φ
Pixels identified as white are considered as noise (Nth / N) > φ
Only pixels in the ‘white’ class is considered as real signals
Experimental results
Background removal and Noise elimination
(a) Before applying background removal and noise elimination method
(b) After applying background removal and noise elimination method
Experimental resultsSegmentation results
Experimental results Block boundary detection and gridding results
Block boundary detection 5 slides (48blocks for each slide) Recall value: 93% Precision value: 100%
Gridding 1 slide (48 blocks) Recall value: 99.97% Precision value: 100%
Experimental resultsSegmentation results
Conclusions
Our proposed method is a fully automatic and highly parallelizable method Handle uneven background and severe noise Detect block boundaries Generate grids Extract spots simply and effectively Highly parallelizable method
Thank you !!